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首页> 外文期刊>Environmental Modelling & Software >Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex
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Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

机译:改进加州Oroville-Thermalito枢纽水电站水库运行的多目标进化优化算法

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摘要

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California. (C) 2014 Elsevier Ltd. All rights reserved.
机译:这项研究演示了改进的进化优化算法(EA),即具有主成分分析和拥挤距离算子(MOSPD)的多目标复杂进化全局优化方法,在Oroville-Thermalito Complex(OTC)的水力发电库中的应用加利福尼亚州水项目(SWP)的重要源头水资源。在OTC的水电联合管理研究中,水电发电的非线性和水库的水位存储关系由多项式函数明确表示,以紧密匹配实际情况并减少线性化近似误差。进行了不同曲线拟合方法之间的比较,以了解简化油藏地形的影响。在优化算法的开发中,采用了拥挤距离和主成分分析技术,以提高朝向目标空间并沿着目标空间的帕累托最优集的最优解的多样性和收敛性。新算法MOSPD,原始多目标复杂进化全局优化方法(MOCOM),多目标差分进化方法(MODE),多目标遗传算法(MOGA),多目标模拟退火之间的比较评估方法(MOSA),并使用基准函数进行了多目标粒子群优化方案(MOPSO)。结果表明,与其他算法相比,最好的MOSPD算法在测试问题上表现出最佳和最一致的性能。新开发的算法(MOSPD)进一步应用于1998年(湿年),2000年(正常年)和2001年(干旱年)的融雪季节的OTC储层释放问题,其中散布和收敛的非支配性更多。 MOSPD的解决方案为决策者提供了更好的运营选择方案,以有效,高效地管理OTC水库,以应对不同的气候,特别是干旱,而干旱在加利福尼亚变得越来越严重和频繁。 (C)2014 Elsevier Ltd.保留所有权利。

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